After being cautious of big data for several years, the marketing and advertising technology sector is adopting it cautiously nowadays. Big data analytics has emerged to be a handy tool for these sectors as it allows a more sophisticated analysis of things to get deeper insights into a consumer's behaviour pattern.  The data is gathered using consumer’s digital footprints like googling and facebook liking etc. Then, this information is used by companies to identify the behaviour of the existing online users to offer them the best targeted deals and services which are specially tailored for them. Therefore, this allows the marketing and advertising sector to create more focused, targeted and, effective campaigns by saving money, targeting the right consumers for the right products and increasing the rate of conversion of the deals offered. Read more at:https://www.entrepreneur.com/article/293678

With the increasing availability of data in the present digital age, a new science of opinion mining is emerging. It is based upon the use of Artificial Intelligence (AI) to mine public opinion for sentiments as well as the topics driving that sentiment. This can be carried out in two ways: the first one involves the exclusive use of AI to structure the data, while the second one involves the use of AI along with processing of data through a team of people to verify the data for sentiment and the topics driving the sentiment, since AI could struggle to understand the nuances of human emotions. So, the field of data mining can be used by governments, global organisations, media and businesses to shape their strategies efficiently and, measure the public’s/consumer’s satisfaction of their policies, products, services and brands.Read more at: http://www.business2community.com/big-data/opinion-mining-future-data-analytics-01849821#SoExOgmWJWYF7RIM.97

Advanced data resources like big data analytics is required by every business. Major areas of sales and marketing getting help from big analytics are customer analytics, operational analytics, product innovation, etc. Relation between big data and mobility allows mutual growth. Mobile apps promotes growth of big data. Apps have all the materials to steer marketing initiatives. Reserve of mobile user data is used to optimize a variety of things. Data driven insights play a major role in mobile marketing. Specific user data along with location data allows more personalization in marketing. High volumes of data is giving more control to analytics and real-time analytics can provide more advantages. Most businesses use data driven marketing approach. Big data pushes the benefits of mobility with advanced approaches. Read more at: http://analyticsindiamag.com/mobile-apps-leverage-big-data-drive-sales-marketing/

The cost of acquiring customer base is very high in the first 12-24 months of business incorporation because we do not know the  characteristics of our target group .This is why Cost to Income Ratio (CIR) is high. To lower CIR we need to collect transactional data, website data and logistics data. Having the data for 8-12 months we can understand the characteristics of existing customers by building up a model and selecting significant variables having an impact. Running a classification algorithm would fine-tune our target group. Customer Lifetime Value (CLV) depicts the quality of customers. Customers with higher CLV would help business to grow.Read more at: http://analyticsindiamag.com/optimize-digital-marketing-spends-drives-traffic-ecommerce-site/

Cybersecurity is a threat to the globe. The quicker it can be dealt with, the better it is. In order to quickly find the problem, the organizations have to deal with the available big data more efficiently. Edge analytics is the concept of extending edge computing to data analysis. The advantage is that analytics can be done closer to the devices which actually generated the data so that the predictive maintenance can be carried out and security problems can easily be dealt with. This is important in situations where waiting for sensor systems to transmit data to a remote cloud is just a wastage of time , for example the case of train safety.Edge analytics can act as a veritable antidote to the malicious act of hackers. Read more at:  http://www.csoonline.com/article/3195922/data-breach/is-edge-analytics-the-front-line-of-cybersecurity.html

One of the challenges faced by IT software development (devops) teams look for faults in a large stack of data. Human mind, with its limited capacity, is incapable to cope up with the dynamic technology and tools which can handle such daily inflows of data. Therefore, a need for centralized analytics system arises to troubleshoot problems and assist in real -time decision making.AI instead of analyzing data itself can mimic other people who had faced similar problems in their system and find out remedies. Cognitive Insights can help protect organizations against Distributed Denial of Service (DDoS)  attacks. Risks would be reduced and customer success rate would improve. Read more at: http://analyticsindiamag.com/see-artificial-intelligence-become-biggest-driver-devops/

The prospect's intent is demonstrated by the frequency of searches on a particular topic from that particular account. Such data is called "Intent Data" .External Intent data( Third -party providers) and Internal data ( CRM and Contact Form) are the sources for such data. It eliminates all the guesswork and saves time by looking out for companies which are researching for our goods and services and then providing them quality content. Till now only 17% companies make use of advances marketing techniques such as intent data. Such knowledge will be a competitive advantage. One of the main advantages is targeting different buyers in the same organization. Overall it would generate more sales.Read more at : http://www.business2community.com/big-data/reach-buyers-matter-intent-data-01851493#d6OimfSHLPcfr9gb.97

Analytics is a cost-effective method of doing business in every domain, be it commercial sector or health sector. By maintaining a hospital database actions can be taken to operate its activities efficiently. Also, there will be increased transparency and reduction in frauds/scams if analytics are used to track any such kind of irrational behavior. Keeping a database of patients' record will avoid repetitive processes and save his time and effort thereby increasing his overall satisfaction. Analytics can help bridge the gap between specialists and patients. Hospital staff appraisal and labor division is another benefit of keeping record of staff data. Read more at: http://readwrite.com/2017/05/16/data-analytics-transforming-healthcare-system-hl1/

Business analytics help in taking decisions. Business intelligence involves analysis of prior analysis and is used to support tactical decision making. Machine learning involves processing historical data to make future predictions. There is scope to add applications to deal with consumers. Unless analysis is done in production application the business will not realize efficiencies. Such analysis can speed up daily activities. The prescriptive analysis makes best use of resources by establishing how processes should be executed. Artificial intelligence supports automation of processes and decision. Machine learning is under AI, but more advanced. The main issue is that the analysis and intelligence should be integrated into the working environment. Methods, culture and discipline will always be the key challenges. Read more at: https://www.gooddata.com/blog/key-strategies-for-profitable-business-analytics

Simply put, cloud computing is the delivery of computing services-servers, storage, databases, networking, software, analytics and the Internet ("the cloud"). In the context of cloud computing, in this blog, we discuss what to expect when you move a business process into a cloud application suite. First, expect to be impressed by the scope of the application, and its configurability. Second, The Cloud, or software as a service delivery model, is much cheaper to execute that an on premise install and upgrade model. Third, expect a fully formed application. This not only means a web based user interface to collect, store, and access your data, but process ware - support for your business processes via workflow, managing processes and states, and "best practices".Read more at

http://it.toolbox.com/blogs/coneblog/8-things-to-expect-from-a-cloud-application-suite-76155

There is no doubt that Artificial Intelligence is impacting all walks of life toady-be it driverless cars, automated surgeries, AI robot and more. AI was recently introduced at the Zurich Insurance company for claims handling and it did the magic- saved 40,000 work hours, while speeding the claim processing time to just five seconds. The insurers are now planning to reap the benefits of this technological advancement and expand the use of AI. The days are not far when AI boats will be deployed to help in setting up policies and process insurance claims. The accuracy is expected to increase over time better performance. For a good read, follow the link below: https://eandt.theiet.org/content/articles/2017/05/ai-adopted-by-zurich-insurance-to-assess-personal-injury-claims/

Dashboard has changed drastically since its beginning and lots of vendors are catering toward big data etc. nowadays. Now the focus has shifted from Excel to Big Data. But it is going to coexist for a long time. A good BI Ecosystem should allow for an easy assemblage of such small sized datasets, whether it be excel/csv or any other text file into a central repository. Visualization will continue to improve and no longer be a differentiating factor among vendors. Users will be able to perform advanced visualization by simple drag and drop operations. Dashboards will continue to be used for monitoring real time events as well as publish scorecards and performance metrics. The line between a dashboard page and any other internal web page will fade. Read more at: http://www.datavizualization.com/blog/what-is-the-future-of-data-visualization-and-dashboard-solutions

Today, "big data" is associated closely with profit maximization techniques (such as recommendation lists on e-commerce sites and targeted ads), high-profile data leaks and privacy issues. However, not all data are bad. Here are the positive side of Data Science:  LOGISTICS: e.g.: Airlines Schedule Flights predict delays based on precise weather forecasts, and other market and political happenings. HEALTHCARE : Big data power the idea of self-learning healthcare programs, which will be able to interpret the data of individual patients: not only their gender, age, weight, and medical history, but also their lifestyle, habits, preferences and give the personalized recommendation about adjustments that would be the most beneficial.  FACE RECOGNITION: Long time ago, it was tough to even think about it, but nowadays, Many tools are there which capture details of the current face and match it with millions of faces in about no time.  Self- Drive Cars : It has made life much easier. Either you are driving or you are on a walk, you don't have to worry at even new places. Many taxi companies are dependent on this and even self-driving cars are also there which is dependent. It finds shortest routes, routes without traffic, etc. in about no time. Read more at: http://www.datasciencecentral.com/profiles/blogs/how-data-science-has-changed-everyday-life-for-the-better

AI or Artificial Intelligence is the hottest sector in India with lots of innovation across sectors. With China is leaping ahead in the AI race on many levels (research papers, R&D, investment and even policies) India is only now waking up to the benefits of AI. If India wants to participate in the AI revolution, then it needs a policy that brings together Indian academicians, researchers, labs, private players and investors on the same platform. Read more at: http://analyticsindiamag.com/india-stand-ai-race-vis-vis-china-us-rest-world/

Nowadays, people are not interested to read huge paragraphs of text. Several researches have shown that Internet users have time to read at most 28% of the word displayed on a web page but most of them read only 20%. On the other hand, visual content has the power to hold the attention longer. If you are just browsing on the Internet as a casual user, not reading all the text wouldn't be a problem. But, when you have a responsibility to process information, things are not that simple. Reasons why the brain needs that kind of visualization: i) You need a visual tier to process large volumes of data. ii) Visual data trigger retentive processing. iii) Visual Data Brings Together All Aspects of Memory.    Visual data presentation relates these three memory functions. When we see the information presented in a visually-attractive way, it triggers our sensory memory and makes it easy for us to process it. Read more at: http://www.datavizualization.com/blog/why-your-brain-needs-data-visualization

In today’s business world, the CRM market is becoming specialized. It’s important for CRM developers or administrators to know the CRM vertical market because they will be able to work more efficiently with business users to implement needs changes, updates, maintenance, and security. The second advantage is that they will understand the business concerns quickly and know how to implement the emerging CRM features and tools to market specific use cases. Read more at: http://it.toolbox.com/blogs/insidecrm/the-case-for-vertical-market-crm-skill-sets-76611

The growing potential of big data should incorporate analytics into its strategy to make better and faster decisions. Businesses can use data and analytics to make better decisions by using granular data. New machine learning has a lot of applications and benefits. Systems enabled by machines can provide better benefits. The technologies could generate productivity gains and an improved quality of life, but they carry the risks associated. Industries are getting greatly influenced by data and analytics, and the effects will become more pronounced when it reaches the masses. Organizations that can harness these capabilities will be able to create significant value and differentiate themselves, whereas other people might not be happy with this. Read more at: http://blogs.economictimes.indiatimes.com/et-citings/managing-analytics/

Management's responsibility must be met instead of hierarchical reporting. Organization should be redefined and opportunities must be addressed. Most improvement opportunities are directly related to deficient business processes and organization silos. Without a strong business process structure, it is nearly impossible to improve performance. There are 12 Business Systems in any organization which are a collection of business processes that align to deliver the goals of the system. A business process transforms the input by value addition into the output. All business transactions take place through business processes that connect in a series to form Business Systems. We typically find somewhere between 8-16 business processes per Business System. A key to performance improvement is to tackle the silos created by traditional organization structures. Read more at: http://www.businessmapping.com/blog/eliminate-organization-silos/

Data Lake helps data scientists by reducing their time taken to gather data and start their real work of data analysis. Copying data physically to one centralized environment may be problematic because storing big data can be costly, copying of data can be prohibited. Metadata describing the data is commonly not copied along with the data and therefore not available to the data scientists. Also, technical and organizational management of a data lake is required. Since data scientists ask for easy and quick data access, a more practical solution to it is a logical data lake. A logical data lake, hides where the data is physically stored and whether it has been copied or not. Logical data lakes can be developed with data virtualization servers such as the Denodo Platform. While, copying and physically storing the data twice is the default approach for the physical data lake, it's optional for the logical data lake. It offers access to data without copying if required and to copy data when needed.  Thus, logical data lake is a better solution for data scientists. Read more at: http://www.datavirtualizationblog.com/data-scientists-physical-data-lakes/

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. Visualization of data helps in finding specific information, like tracing data correlations by presenting the data in graphic form, and noticing how one set of data influences another. Also, by live interaction with data one can spot the changes in the data as it happens and get a predictive analysis. Data visualization enables one to not only see the information, but also to know the reasons behind it. With predictive analysis, the behavior of the trends in the future can be predicted. Thus, data visualization tools have become a necessity in modern data analysis. Read more at: http://www.datavizualization.com/blog/the-top-5-benefits-of-using-data-visualization